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Designs and builds AI-native, cloud-based enterprise apps using LLMs, RAG, vector DBs, and agentic workflows for SAP customers in Dubai.
Builds and deploys production AI systems like voice hardware, conversational agents, and RAG pipelines using Python, FastAPI, and ML frameworks.
Build, deploy, and monitor ML/AI models and GenAI systems using Python, TensorFlow, PyTorch, and MLOps tooling like MLflow and Kubernetes.
Designs and deploys LLM-powered agents and copilots, builds RAG pipelines, and integrates vector databases for client workflows.
Lead the design and deployment of advanced AI models, including LLMs and multimodal systems, using deep learning and statistical methods to solve complex business and research challenges.
Build and test AI-powered airline tools using Python, LLMs, and agentic workflows; validate accuracy, safety, and performance of generative AI systems for aviation operations.
Lead the AI engineering squad and design the core AI architecture for a unified, AI-powered fintech platform integrating LLMs, retrieval systems, and agentic workflows into financial services.
Build and deploy AI/ML models on Databricks to analyze Miral Destinations data and deliver actionable insights for business decisions.
Builds and scales APIs, databases, and AI integrations for an AI-powered interview platform using Python, FastAPI, and PostgreSQL.
Build and own the cloud, CI/CD, and reliability foundation for AI-powered workflows at an early-stage startup, scaling from client deployments to a repeatable operating model.
Builds and scales cloud-native backend services in Python/Node.js, using microservices, Docker, Kubernetes, and Azure, with a focus on security and performance.
Build and deploy AI systems using semantic search, RAG, and multi-agent workflows with LLMs and NLP/NLU techniques in Python and cloud platforms.
Build and deploy production-grade AI/ML models using Python, TensorFlow, PyTorch, and MLOps tooling for scalable, real-world applications.
Ship production-grade LLM and automation systems that accelerate eSource-to-EDC workflows — with the validation rigor and audit traceability that clinical research demands. Pakistan Lahore About the role Nexa Trials is…
Build and ship LLM-powered features for enterprise clients, including RAG systems, AI agents, and vector-based retrieval pipelines using Python, LangChain, and cloud tools.
Build and optimize GenAI systems using LLMs, prompt engineering, RAG, and agent workflows with frameworks like LangChain. Integrate APIs, vector databases, and external tools for scalable AI solutions.
Designs the architecture for self-learning AI agents and GenAI systems, focusing on orchestration, reasoning layers, and scalable agentic workflows.
Build and scale AI agents and ML pipelines using Python, PyTorch/TensorFlow, and frameworks like LangChain; integrate LLMs, vector DBs, and cloud-native systems.
Build and deploy AI-powered insurance workflows using LLMs and agent-based automation to process submissions, policies, and client communications.
Build and deploy multi-agent AI systems using LLM APIs (Bedrock, OpenAI, Mistral), LangGraph/LangChain, and AWS serverless tools; focus on execution, not design.
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